o }oi @s:ddlmZ   d dedededed eef d d Zd S))Listlinear num_layersstochastic_depth_drop_probstochastic_depth_modestochastic_depth_start_layerreturncsdkr dkstdtdd|kr|ks$tdtddg|}||dkr\|dkrH|fdd tddD7}|S|d krU|g7}|Std ||S) arComputes drop probabilities for stochastic depth regularization technique. The first layer is never dropped and the starting layer needs to be greater or equal to 1. Args: num_layers (int): number of layers in the network. stochastic_depth_drop_prob (float): if non-zero, will randomly drop layers during training. The higher this value, the more often layers are dropped. Defaults to 0.0. stochastic_depth_mode (str): can be either "linear" or "uniform". If set to "uniform", all layers have the same probability of drop. If set to "linear", the drop probability grows linearly from 0 for the first layer to the desired value for the final layer. Defaults to "linear". stochastic_depth_start_layer (int): starting layer for stochastic depth. All layers before this will never be dropped. Note that drop probability will be adjusted accordingly if mode is "linear" when start layer is > 1. Defaults to 1. Returns: List[float]: list of drop probabilities for all layers rg?z/stochastic_depth_drop_prob has to be in [0, 1).rz:stochastic_depth_start_layer has to be in [1, num layers].rrcsg|]}|qSr ).0lLrr i/home/ubuntu/.local/lib/python3.10/site-packages/nemo/collections/asr/parts/utils/regularization_utils.py 9sz7compute_stochastic_depth_drop_probs..uniformzMstochastic_depth_mode has to be one of ["linear", "uniform"]. Current value: ) ValueErrorrange)rrrr layer_drop_probsr rr#compute_stochastic_depth_drop_probss( "rN)rrr)typingrintfloatstrrr r r rs